jr oakes

Over the last several years, Google has slowly reduced the amount of data available to SEO practitioners. First it was keyword data, then PageRank score. Now it is specific search volume from AdWords (unless you are spending some moola). You can read more about this in Russ Jones’s excellent article that details the impact of his company’s research and insights into clickstre ...

Today, the SEO world is abuzz with the term “relevancy.” Google has gone well past keywords and their frequency to looking at the meaning imparted by the words and how they relate to the query at hand. In fact, for years, the common term used for working with text and language had been natural language processing (NLP). The new focus, though, is natural language understanding (NLU).

Introduction Over the last few months, we have been working with a company named Statec (a data science company from Brazil) to engineer features for predictive algorithms. One of the initial considerations in working with predictive algorithms is picking relevant data to train them on. We set out quite naively to put together a list of webpage features that we thought may offer some value.

Machine learning is quickly becoming an indispensable tool for many large companies. Everyone has, for sure, heard about Google’s AI algorithm beating the World Champion in Go, as well as technologies like RankBrain, but machine learning does not have to be a mystical subject relegated to the domain of math researchers.